Greedy Learning of Binary Latent Trees

By Stefan Harmeling, Christopher K. I. Williams. 2011

56 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Molecular Biology, Probabilistic & Bayesian Methods

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yoshua Bengio
2. Benigno Uria
3. Ruslan Salakhutdinov
4. Karol Gregor
5. Hugo Larochelle
6. Iain Murray
7. Kyunghyun Cho
8. Sergey Levine

In Molecular Biology

1. Tae-Kyun Kim
2. Iain Murray
3. Hugo Larochelle
4. Hyung Jin Chang
5. Danhang Tang
6. Alykhan Tejani

In Probabilistic & Bayesian Methods

1. Benigno Uria
2. Yoshua Bengio
3. Karol Gregor
4. Hugo Larochelle
5. Iain Murray
6. Marc-Alexandre Côté
7. Ruslan Salakhutdinov
8. Danilo Jimenez Rezende

Citations in the corpus

• Neural Autoregressive Distribution Estimation (2016, methodology): “We adapted an implementation provided by Harmeling and Williams (2011), who found Chow–Liu to be a strong baseline.”
• Latent Regression Forest: Structured Estimation of 3D Articulated Hand Posture (2014, methodology): “Latent Tree Models, in particular, are interesting as they are able to represent complex relationships in the data [11] and, furthermore, recent methods for constructing these models ([2, 7]) enable us to learn consistent and minimal latent tree models in a computationally efficient manner.”
• Kernel Embeddings of Latent Tree Graphical Models (2011, unknown): “Experiments on simulated and real data show the advantage of our proposed approach.”